#region License Information
/* HeuristicLab
* Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System;
using System.Collections.Generic;
using System.Linq;
namespace HeuristicLab.Problems.Instances.DataAnalysis {
public class KotanchekFunction : ArtificialRegressionDataDescriptor {
public override string Name { get { return "Vladislavleva-1 F1(X1,X2) = exp(-(X1 - 1))² / (1.2 + (X2 -2.5)²"; } }
public override string Description {
get {
return "Paper: Order of Nonlinearity as a Complexity Measure for Models Generated by Symbolic Regression via Pareto Genetic Programming " + Environment.NewLine
+ "Authors: Ekaterina J. Vladislavleva, Member, IEEE, Guido F. Smits, Member, IEEE, and Dick den Hertog" + Environment.NewLine
+ "Function: F1(X1, X2) = exp(-(X1 - 1))² / (1.2 + (X2 -2.5)²" + Environment.NewLine
+ "Training Data: 100 points X1, X2 = Rand(0.3, 4)" + Environment.NewLine
+ "Test Data: 45*45 points (X1, X2) = (-0.2:0.1:4.2)" + Environment.NewLine
+ "Function Set: +, -, *, /, square, e^x, e^-x, x^eps, x + eps, x * eps";
}
}
protected override string TargetVariable { get { return "Y"; } }
protected override string[] VariableNames { get { return new string[] { "X1", "X2", "Y" }; } }
protected override string[] AllowedInputVariables { get { return new string[] { "X1", "X2" }; } }
protected override int TrainingPartitionStart { get { return 0; } }
protected override int TrainingPartitionEnd { get { return 100; } }
protected override int TestPartitionStart { get { return 100; } }
protected override int TestPartitionEnd { get { return 100 + (45 * 45); } }
protected override List> GenerateValues() {
List> data = new List>();
List oneVariableTestData = ValueGenerator.GenerateSteps(-0.2, 4.2, 0.1).ToList();
List> testData = new List>() { oneVariableTestData, oneVariableTestData };
var combinations = ValueGenerator.GenerateAllCombinationsOfValuesInLists(testData).ToList>();
for (int i = 0; i < AllowedInputVariables.Count(); i++) {
data.Add(ValueGenerator.GenerateUniformDistributedValues(100, 0.3, 4).ToList());
data[i].AddRange(combinations[i]);
}
double x1, x2;
List results = new List();
for (int i = 0; i < data[0].Count; i++) {
x1 = data[0][i];
x2 = data[1][i];
results.Add(Math.Exp(-Math.Pow(x1 - 1, 2)) / (1.2 + Math.Pow(x2 - 2.5, 2)));
}
data.Add(results);
return data;
}
}
}